Event Date Details:
refreshments served at 3:15 p.m.
- Sobel Seminar Room; South Hall 5607F
- Department Seminar Series
Sang-Yun Oh (PSTAT-UCSB)
Extreme scale graphical model selection and an application in data-driven discovery
Abstract: Graphical Models is a useful framework for representing conditional dependencies in multivariate data. Model selection in this setting, also called structure learning in computer science, refers to recovering conditional dependency structure often by solving an optimization problem. This talk will give an overview of two on-going efforts related to graphical model selection. First part of the talk will describe our proposed approach to â€œscale-upâ€ an existing model selection method to work in extremely high dimensional settings. Our approach is a combination of proximal gradient-based optimization algorithm and communication-avoiding sparse-dense matrix multiplication algorithm. The linear algebra routine is specifically designed for distributed memory parallel computing environment to further increase scalability. Second part will describe a data-driven discovery process for parcellating brain into functional regions in which the proposed algorithm plays an important role. Preliminary results from analysis of resting state functional MRI scans will also be discussed.